miχpods: MOM6

• test out hvplot

• build LES catalog

• bootstrap error on mean, median?

• Try daily vs hourly

• add TAO χpods

• fix EUC max at 110

Questions:

• N2 vs N2T

• match time intervals

• match vertical resolution

• restrict TAO S2 to ADCP depth range

• restrict to top 200m.

References

Warner & Moum (2019)

Setup

%load_ext watermark

import datetime
import glob
import os

import cf_xarray
import dask
import dcpy
import distributed
import flox.xarray
import holoviews as hv
import hvplot.xarray
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import xarray as xr
import xgcm

import pump
from pump import mixpods

hv.notebook_extension('bokeh')

dask.config.set({"array.slicing.split_large_chunks": False})
plt.rcParams["figure.dpi"] = 140 
plt.style.use("bmh")
xr.set_options(keep_attrs=True, display_expand_data=False)

gcmdir = "/glade/campaign/cgd/oce/people/bachman/TPOS_1_20_20_year/OUTPUT/"  # MITgcm output directory
stationdirname = gcmdir

%watermark -iv

The watermark extension is already loaded. To reload it, use:
  %reload_ext watermark
hvplot       : 0.8.0
cf_xarray    : 0.7.4
ncar_jobqueue: 2021.4.14
ipywidgets   : 7.7.1
dask_jobqueue: 0.7.3
xgcm         : 0.6.0
json         : 2.0.9
dask         : 2022.7.0
matplotlib   : 3.5.2
distributed  : 2022.7.0
pandas       : 1.4.3
dcpy         : 0.1.dev385+g121534c
xarray       : 2022.6.0rc0
numpy        : 1.22.4
sys          : 3.10.5 | packaged by conda-forge | (main, Jun 14 2022, 07:04:59) [GCC 10.3.0]
pump         : 0.1
flox         : 0.5.9

import ncar_jobqueue

if "client" in locals():
    client.close()
    del client
if "cluster" in locals():
    cluster.close()

#env = {"OMP_NUM_THREADS": "3", "NUMBA_NUM_THREADS": "3"}

# cluster = distributed.LocalCluster(
#    n_workers=8,
#    threads_per_worker=1,
#    env=env
# )

if "cluster" in locals():
    del cluster

#cluster = ncar_jobqueue.NCARCluster(
#    project="NCGD0011",
#    scheduler_options=dict(dashboard_address=":9797"),
#)
# cluster = dask_jobqueue.PBSCluster(
#    cores=9, processes=9, memory="108GB", walltime="02:00:00", project="NCGD0043",
#    env_extra=env,
# )

import dask_jobqueue

cluster = dask_jobqueue.PBSCluster(
    cores=1, # The number of cores you want
    memory='23GB', # Amount of memory
    processes=1, # How many processes
    queue='casper', # The type of queue to utilize (/glade/u/apps/dav/opt/usr/bin/execcasper)
    local_directory='$TMPDIR', # Use your local directory
    resource_spec='select=1:ncpus=1:mem=23GB', # Specify resources
    project='ncgd0011', # Input your project ID here
    walltime='02:00:00', # Amount of wall time
    interface='ib0', # Interface to use
)

cluster.adapt(minimum_jobs=2, maximum_jobs=12)

client = distributed.Client(cluster)
client

Client

Client-5bc08d4d-11b7-11ed-94b4-3cecef1b12bc

Connection method: Cluster object	Cluster type: dask_jobqueue.PBSCluster
Dashboard: https://jupyterhub.hpc.ucar.edu/dev/user/dcherian/proxy/8787/status

Cluster Info

PBSCluster

414f1f1e

Dashboard: https://jupyterhub.hpc.ucar.edu/dev/user/dcherian/proxy/8787/status	Workers: 0
Total threads: 0	Total memory: 0 B

Scheduler Info

Scheduler

Scheduler-967ab8bc-d7cc-4f5c-9367-619b430baf67

Comm: tcp://10.12.206.55:35183	Workers: 0
Dashboard: https://jupyterhub.hpc.ucar.edu/dev/user/dcherian/proxy/8787/status	Total threads: 0
Started: Just now	Total memory: 0 B

Workers

dask.config.set(scheduler=client)

<dask.config.set at 0x2b150da735b0>

Read data

TAO

tao_gridded = (
    xr.open_dataset(
        os.path.expanduser("~/work/pump/zarrs/tao-gridded-ancillary.zarr"), chunks="auto", engine="zarr"
    )
    .sel(longitude=-140, time=slice("2005-Jun", "2015"))
)
tao_gridded["depth"].attrs["axis"] = "Z"
# eucmax exists
tao_gridded.coords["eucmax"] = pump.calc.get_euc_max(tao_gridded.u.reset_coords(drop=True), kind="data")
#pump.calc.calc_reduced_shear(tao_gridded)
tao_gridded.coords["enso_transition"] = pump.obs.make_enso_transition_mask().reindex(time=tao_gridded.time, method="nearest")
tao_gridded.u.cf.plot()
tao_gridded.eucmax.plot()

/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/dataset.py:245: UserWarning: The specified Dask chunks separate the stored chunks along dimension "depth" starting at index 58. This could degrade performance. Instead, consider rechunking after loading.
  warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/dataset.py:245: UserWarning: The specified Dask chunks separate the stored chunks along dimension "time" starting at index 139586. This could degrade performance. Instead, consider rechunking after loading.
  warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/dataset.py:245: UserWarning: The specified Dask chunks separate the stored chunks along dimension "longitude" starting at index 2. This could degrade performance. Instead, consider rechunking after loading.
  warnings.warn(

[<matplotlib.lines.Line2D at 0x2b150d367d30>]

tao_gridded = (
    tao_gridded.update({
        "n2s2pdf": mixpods.pdf_N2S2(
            tao_gridded[["S2", "N2T"]].drop_vars(["shallowest", "zeuc"]).rename_vars({"N2T": "N2"})
        ).load()
    }
    )
)

tao_gridded.coords["oni"] = pump.obs.process_oni()
tao_gridded.coords["enso_transition_mask"] = mixpods.make_enso_transition_mask(tao_gridded.oni)

tao_Ri = xr.load_dataarray("tao-hourly-Ri-seasonal-percentiles.nc").cf.guess_coord_axis()

MITgcm stations

metrics = pump.model.read_metrics(stationdirname)
stations = pump.model.read_stations_20(stationdirname)

gcmeq = stations.sel(latitude=0, longitude=[-155.025, -140.025, -125.025, -110.025], method="nearest")
#enso = pump.obs.make_enso_mask()
#mitgcm["enso"] = enso.reindex(time=mitgcm.time.data, method="nearest")

gcmeq["eucmax"] = pump.calc.get_euc_max(gcmeq.u)
pump.calc.calc_reduced_shear(gcmeq)

oni = pump.obs.process_oni()
gcmeq["enso_transition"] = mixpods.make_enso_transition_mask(oni).reindex(time=gcmeq.time.data, method="nearest")


mitgcm = gcmeq.sel(longitude=-140.025, method="nearest")

#metrics_ = xr.align(metrics, mitgcm.expand_dims(["latitude", "longitude"]), join="inner")[0].squeeze()


mitgcm = mitgcm.update({"n2s2pdf": mixpods.pdf_N2S2(mitgcm).load(scheduler=client)})

calc uz
calc vz
calc S2
calc N2
calc shred2
Calc Ri

gcmeq.eucmax.hvplot.line(x="time", by="longitude")

MOM6 run : calculate ONI

dirname = "/glade/scratch/dcherian/archive/gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.001.mixpods/ocn/hist"
static = xr.open_dataset(*glob.glob(f"{dirname}/*static*.nc"))
sfc = xr.open_mfdataset(
    sorted(glob.glob(f"{dirname}/*sfc*")),
    coords="minimal",
    data_vars="minimal",
    compat="override",
    use_cftime=True,
    parallel=True,
)
sfc["time"] = sfc.time + datetime.timedelta(days=365*1957)
#sfc["time"] = sfc.time + xr.coding.cftime_offsets.YearBegin(1957)
sfc.coords.update(static.drop("time"))

#sfc["tos"].attrs["coordinates"] = "geolon geolat"
sst = sfc.cf["sea_surface_temperature"]
sst.cf

Coordinates:
- CF Axes: * X: ['xh']
           * Y: ['yh']
           * T: ['time']
             Z: n/a

- CF Coordinates: * longitude: ['xh']
                  * latitude: ['yh']
                  * time: ['time']
                    vertical: n/a

- Cell Measures:   area: ['areacello']
                   volume: n/a

- Standard Names:   cell_area: ['areacello']

- Bounds:   n/a

# Calculate a monthly average sea surface temperature in the Nino 3.4 region (5°S-5°N, 170°W-120°W).
monthly_ = (
    sst.cf.sel(latitude=slice(-5, 5), longitude=slice(-170, -120))
    .cf.mean(["X", "Y"])
    .resample(time="M")
    .mean()
    #.sel(time=slice("1960", "1999-12-31"))
    #.reindex(
    #    time=xr.cftime_range("1955-01-01", monthly.time[-1].dt.strftime("%Y-%m-%d").data.item(), freq="MS")
    #)
    .load()
)

monthly = (
    monthly_
    .reindex(
        time=xr.cftime_range(
            "1956-01-01",
            monthly_.time[-1].dt.strftime("%Y-%m-%d").data.item(), 
            freq="M", 
            calendar=monthly_.time.dt.calendar,
        )
    )
    .convert_calendar("gregorian", use_cftime=False)
)
#monthly = monthly_
monthly.plot()

[<matplotlib.lines.Line2D at 0x2b153f9f4df0>]

monthly.coords["oni"] = mixpods.calc_oni(monthly)
monthly.coords["enso_transition_mask"] = mixpods.make_enso_transition_mask(monthly.oni)

(
    oniobs.hvplot.line(x="time", label="obs")
    * onimom6.hvplot.line(x="time", label="MOM6")
).opts(ylabel="ONI [°C]")

oniobs.enso_transition_mask.plot()
onimom6.enso_transition_mask.plot(color='r')

[<matplotlib.lines.Line2D at 0x2b6d7fd20eb0>]

MOM6 sections

import mom6_tools
import xgcm
from mom6_tools.sections import combine_nested, read_raw_files
from mom6_tools.wright_eos import wright_eos


def combine_variables_by_coords(dsets):
    import itertools

    import tqdm

    all_vars = set(itertools.chain(*[ds.data_vars for ds in dsets]))

    merged = xr.merge(
        xr.combine_by_coords([ds[var] for ds in dsets if ds.sizes["time"] > 0], combine_attrs="override")
        for var in tqdm.tqdm(all_vars)
    )
    
    return merged

import glob

# dirname = "/glade/scratch/gmarques/gmom.e23.GJRAv3.TL319_t061_zstar_N65.mixpods.001/run"

dirname = "/glade/scratch/dcherian/gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.001.mixpods/run/"
files = sorted(glob.glob(f"{dirname}/*TAO*140W*.nc.*"))

lons = [140]  # [90, 110, 140, 155, 170]  # TODO: Add 125
dsets = read_raw_files(files, parallel=True)

combined = combine_variables_by_coords(dsets)

#mom6tao = read_tao_sections(dirname)

def interp_to_center(ds):
    import toolz as tlz

    ix0 = np.arange(1, ds.sizes["xq"], 4)
    ix1 = ix0 + 1
    indices = list(tlz.interleave([ix0, ix1]))

    # print(ds.xq.data[indices])
    out = (
        ds
        #.isel(xq=[1, 2, 5, 6, 8, 9, 12, 13, 16, 17])
        .isel(xq=[1, 2])
        .coarsen(xq=2).mean()
    )
    out = out.sel(xh = out.xq.data, method="nearest", tolerance=0.05)
    
    for var in out:
        if "xq" in out[var].dims:
            out[var] = out[var].rename({"xq": "xh"}).drop("xh")
    return out.drop_vars("xq")


mom6tao = interp_to_center(combined).cf.chunk({"Y": -1})
mom6tao["dens"] = wright_eos(mom6tao.thetao, mom6tao.so, 0)
mom6tao["dens"].attrs.update(
    {"units": "kg/m^3", "standard_name": "sea_water_potential_density"}
)
mom6tao

100%|██████████| 21/21 [02:21<00:00,  6.76s/it]

<xarray.Dataset>
Dimensions:           (time: 480600, yh: 49, xh: 1, zi: 66, zl: 65, nv: 2,
                       yq: 49)
Coordinates:
  * time              (time) object 0001-01-06 00:30:00 ... 0056-01-05 23:30:00
  * yh                (yh) float64 -5.911 -5.664 -5.417 ... 5.541 5.788 6.035
  * xh                (xh) float64 -140.0
  * zi                (zi) float64 0.0 2.5 5.0 7.5 ... 5.503e+03 5.751e+03 6e+03
  * zl                (zl) float64 1.25 3.75 6.25 ... 5.627e+03 5.876e+03
  * nv                (nv) float64 1.0 2.0
  * yq                (yq) float64 -6.035 -5.788 -5.541 ... 5.417 5.664 5.911
Data variables: (12/22)
    average_T2        (time) object dask.array<chunksize=(8640,), meta=np.ndarray>
    KPP_kheat         (time, zi, yh, xh) float32 dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>
    volcello          (time, zl, yh, xh) float32 dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>
    net_heat_surface  (time, yh, xh) float32 dask.array<chunksize=(8640, 49, 1), meta=np.ndarray>
    Kv_u              (time, zl, yh, xh) float32 dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>
    time_bnds         (time, nv) timedelta64[ns] dask.array<chunksize=(8640, 2), meta=np.ndarray>
    ...                ...
    zos               (time, yh, xh) float32 dask.array<chunksize=(8640, 49, 1), meta=np.ndarray>
    KPP_OBLdepth      (time, yh, xh) float32 dask.array<chunksize=(8640, 49, 1), meta=np.ndarray>
    vo                (time, zl, yq, xh) float32 dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>
    average_DT        (time) timedelta64[ns] dask.array<chunksize=(8640,), meta=np.ndarray>
    tauy              (time, yq, xh) float32 dask.array<chunksize=(8640, 49, 1), meta=np.ndarray>
    dens              (time, zl, yh, xh) float32 dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • time: 480600
  • yh: 49
  • xh: 1
  • zi: 66
  • zl: 65
  • nv: 2
  • yq: 49
• Coordinates: (7)
  • time
    (time)
    object
    0001-01-06 00:30:00 ... 0056-01-...

    long_name :

    time

    cartesian_axis :

    T

    calendar_type :

    NOLEAP

    bounds :

    time_bnds

    array([cftime.DatetimeNoLeap(1, 1, 6, 0, 30, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1, 1, 6, 1, 30, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1, 1, 6, 2, 30, 0, 0, has_year_zero=True), ...,
           cftime.DatetimeNoLeap(56, 1, 5, 21, 30, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(56, 1, 5, 22, 30, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(56, 1, 5, 23, 30, 0, 0, has_year_zero=True)],
          dtype=object)

  • yh
    (yh)
    float64
    -5.911 -5.664 ... 5.788 6.035

    long_name :

    h point nominal latitude

    units :

    degrees_north

    cartesian_axis :

    Y

    domain_decomposition :

    [210 258 210 221]

    array([-5.911442, -5.664167, -5.416807, -5.16932 , -4.921678, -4.673864,
           -4.425866, -4.177682, -3.929311, -3.680757, -3.432028, -3.183131,
           -2.934077, -2.684877, -2.435542, -2.186084, -1.936516, -1.68685 ,
           -1.437098, -1.187273, -0.937388, -0.687456, -0.437489, -0.187499,
            0.0625  ,  0.312496,  0.562476,  0.812427,  1.062338,  1.312194,
            1.561984,  1.811694,  2.061313,  2.310828,  2.560226,  2.809495,
            3.058623,  3.3076  ,  3.556414,  3.805057,  4.05352 ,  4.301798,
            4.549888,  4.797793,  5.04552 ,  5.293081,  5.540501,  5.787812,
            6.035065])

  • xh
    (xh)
    float64
    -140.0

    long_name :

    h point nominal longitude

    units :

    degrees_east

    cartesian_axis :

    X

    domain_decomposition :

    [220 222 220 221]

    array([-140.])

  • zi
    (zi)
    float64
    0.0 2.5 5.0 ... 5.751e+03 6e+03

    long_name :

    Interface pseudo-depth, -z*

    units :

    meter

    cartesian_axis :

    Z

    positive :

    down

    array([0.00000e+00, 2.50000e+00, 5.00000e+00, 7.50000e+00, 1.00000e+01,
           1.27700e+01, 1.61500e+01, 2.01600e+01, 2.48100e+01, 3.01000e+01,
           3.60500e+01, 4.26600e+01, 4.99400e+01, 5.79100e+01, 6.65700e+01,
           7.59400e+01, 8.60200e+01, 9.68300e+01, 1.08370e+02, 1.20660e+02,
           1.33720e+02, 1.47570e+02, 1.62260e+02, 1.77850e+02, 1.94410e+02,
           2.12020e+02, 2.30780e+02, 2.50800e+02, 2.72220e+02, 2.95220e+02,
           3.19990e+02, 3.46780e+02, 3.75880e+02, 4.07640e+02, 4.42510e+02,
           4.81010e+02, 5.23800e+02, 5.71700e+02, 6.25710e+02, 6.87080e+02,
           7.57330e+02, 8.38280e+02, 9.32030e+02, 1.04083e+03, 1.16687e+03,
           1.31191e+03, 1.47672e+03, 1.66077e+03, 1.86211e+03, 2.07777e+03,
           2.30441e+03, 2.53891e+03, 2.77875e+03, 3.02206e+03, 3.26758e+03,
           3.51446e+03, 3.76218e+03, 4.01041e+03, 4.25895e+03, 4.50768e+03,
           4.75652e+03, 5.00516e+03, 5.25384e+03, 5.50255e+03, 5.75127e+03,
           6.00000e+03])

  • zl
    (zl)
    float64
    1.25 3.75 ... 5.627e+03 5.876e+03

    long_name :

    Layer pseudo-depth, -z*

    units :

    meter

    cartesian_axis :

    Z

    positive :

    down

    array([1.250000e+00, 3.750000e+00, 6.250000e+00, 8.750000e+00, 1.138500e+01,
           1.446000e+01, 1.815500e+01, 2.248500e+01, 2.745500e+01, 3.307500e+01,
           3.935500e+01, 4.630000e+01, 5.392500e+01, 6.224000e+01, 7.125500e+01,
           8.098000e+01, 9.142500e+01, 1.026000e+02, 1.145150e+02, 1.271900e+02,
           1.406450e+02, 1.549150e+02, 1.700550e+02, 1.861300e+02, 2.032150e+02,
           2.214000e+02, 2.407900e+02, 2.615100e+02, 2.837200e+02, 3.076050e+02,
           3.333850e+02, 3.613300e+02, 3.917600e+02, 4.250750e+02, 4.617600e+02,
           5.024050e+02, 5.477500e+02, 5.987050e+02, 6.563950e+02, 7.222050e+02,
           7.978050e+02, 8.851550e+02, 9.864300e+02, 1.103850e+03, 1.239390e+03,
           1.394315e+03, 1.568745e+03, 1.761440e+03, 1.969940e+03, 2.191090e+03,
           2.421660e+03, 2.658830e+03, 2.900405e+03, 3.144820e+03, 3.391020e+03,
           3.638320e+03, 3.886295e+03, 4.134680e+03, 4.383315e+03, 4.632100e+03,
           4.880840e+03, 5.129500e+03, 5.378195e+03, 5.626910e+03, 5.875635e+03])

  • nv
    (nv)
    float64
    1.0 2.0

    long_name :

    vertex number

    units :

    none

    cartesian_axis :

    N

    array([1., 2.])

  • yq
    (yq)
    float64
    -6.035 -5.788 ... 5.664 5.911

    long_name :

    q point nominal latitude

    units :

    degrees_north

    cartesian_axis :

    Y

    domain_decomposition :

    [209 257 209 221]

    array([-6.035065, -5.787812, -5.540501, -5.293081, -5.04552 , -4.797793,
           -4.549888, -4.301798, -4.05352 , -3.805057, -3.556414, -3.3076  ,
           -3.058623, -2.809495, -2.560226, -2.310828, -2.061313, -1.811694,
           -1.561984, -1.312194, -1.062338, -0.812427, -0.562476, -0.312496,
           -0.0625  ,  0.187499,  0.437489,  0.687456,  0.937388,  1.187273,
            1.437098,  1.68685 ,  1.936516,  2.186084,  2.435542,  2.684877,
            2.934077,  3.183131,  3.432028,  3.680757,  3.929311,  4.177682,
            4.425866,  4.673864,  4.921678,  5.16932 ,  5.416807,  5.664167,
            5.911442])

• Data variables: (22)
  • average_T2
    (time)
    object
    dask.array<chunksize=(8640,), meta=np.ndarray>

    long_name :

    End time for average period

    Array Chunk Bytes 3.67 MiB 68.44 kiB Shape (480600,) (8760,) Count 168 Tasks 56 Chunks Type object numpy.ndarray

  • KPP_kheat
    (time, zi, yh, xh)
    float32
    dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>

    long_name :

    Heat diffusivity due to KPP, as calculated by [CVMix] KPP

    units :

    m2/s

    cell_methods :

    area:mean zi:point yh:mean xh:mean time: mean

    cell_measures :

    area: areacello

    time_avg_info :

    average_T1,average_T2,average_DT

    Array Chunk Bytes 5.79 GiB 65.50 MiB Shape (480600, 66, 49, 1) (8760, 40, 49, 1) Count 4592 Tasks 112 Chunks Type float32 numpy.ndarray

  • volcello
    (time, zl, yh, xh)
    float32
    dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>

    long_name :

    Ocean grid-cell volume

    units :

    m3

    cell_methods :

    area:sum zl:sum yh:sum xh:sum time: mean

    time_avg_info :

    average_T1,average_T2,average_DT

    standard_name :

    ocean_volume

    Array Chunk Bytes 5.70 GiB 65.50 MiB Shape (480600, 65, 49, 1) (8760, 40, 49, 1) Count 4592 Tasks 112 Chunks Type float32 numpy.ndarray

  • net_heat_surface
    (time, yh, xh)
    float32
    dask.array<chunksize=(8640, 49, 1), meta=np.ndarray>

    long_name :

    Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments

    units :

    W m-2

    cell_methods :

    area:mean yh:mean xh:mean time: mean

    cell_measures :

    area: areacello

    time_avg_info :

    average_T1,average_T2,average_DT

    standard_name :

    surface_downward_heat_flux_in_sea_water

    Array Chunk Bytes 89.83 MiB 1.64 MiB Shape (480600, 49, 1) (8760, 49, 1) Count 2520 Tasks 56 Chunks Type float32 numpy.ndarray

  • Kv_u
    (time, zl, yh, xh)
    float32
    dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>

    long_name :

    Total vertical viscosity at u-points

    units :

    m2 s-1

    cell_methods :

    zl:mean yh:mean xq:point time: mean

    time_avg_info :

    average_T1,average_T2,average_DT

    interp_method :

    none

    Array Chunk Bytes 5.70 GiB 65.50 MiB Shape (480600, 65, 49, 1) (8760, 40, 49, 1) Count 5936 Tasks 112 Chunks Type float32 numpy.ndarray

  • time_bnds
    (time, nv)
    timedelta64[ns]
    dask.array<chunksize=(8640, 2), meta=np.ndarray>

    long_name :

    time axis boundaries

    calendar :

    NOLEAP

    Array Chunk Bytes 7.33 MiB 136.88 kiB Shape (480600, 2) (8760, 2) Count 168 Tasks 56 Chunks Type timedelta64[ns] numpy.ndarray

  • h
    (time, zl, yh, xh)
    float32
    dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>

    long_name :

    Layer Thickness

    units :

    m

    cell_methods :

    area:mean zl:sum yh:mean xh:mean time: mean

    cell_measures :

    volume: volcello area: areacello

    time_avg_info :

    average_T1,average_T2,average_DT

    Array Chunk Bytes 5.70 GiB 65.50 MiB Shape (480600, 65, 49, 1) (8760, 40, 49, 1) Count 4592 Tasks 112 Chunks Type float32 numpy.ndarray

  • average_T1
    (time)
    object
    dask.array<chunksize=(8640,), meta=np.ndarray>

    long_name :

    Start time for average period

    Array Chunk Bytes 3.67 MiB 68.44 kiB Shape (480600,) (8760,) Count 168 Tasks 56 Chunks Type object numpy.ndarray

  • Kd_heat
    (time, zi, yh, xh)
    float32
    dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>

    long_name :

    Total diapycnal diffusivity for heat at interfaces

    units :

    m2 s-1

    cell_methods :

    area:mean zi:point yh:mean xh:mean time: mean

    cell_measures :

    area: areacello

    time_avg_info :

    average_T1,average_T2,average_DT

    Array Chunk Bytes 5.79 GiB 65.50 MiB Shape (480600, 66, 49, 1) (8760, 40, 49, 1) Count 4592 Tasks 112 Chunks Type float32 numpy.ndarray

  • SW
    (time, yh, xh)
    float32
    dask.array<chunksize=(8640, 49, 1), meta=np.ndarray>

    long_name :

    Shortwave radiation flux into ocean

    units :

    W m-2

    cell_methods :

    area:mean yh:mean xh:mean time: mean

    cell_measures :

    area: areacello

    time_avg_info :

    average_T1,average_T2,average_DT

    standard_name :

    net_downward_shortwave_flux_at_sea_water_surface

    Array Chunk Bytes 89.83 MiB 1.64 MiB Shape (480600, 49, 1) (8760, 49, 1) Count 2520 Tasks 56 Chunks Type float32 numpy.ndarray

  • taux
    (time, yh, xh)
    float32
    dask.array<chunksize=(8640, 49, 1), meta=np.ndarray>

    long_name :

    Zonal surface stress from ocean interactions with atmos and ice

    units :

    Pa

    cell_methods :

    yh:mean xq:point time: mean

    time_avg_info :

    average_T1,average_T2,average_DT

    standard_name :

    surface_downward_x_stress

    interp_method :

    none

    Array Chunk Bytes 89.83 MiB 1.64 MiB Shape (480600, 49, 1) (8760, 49, 1) Count 3192 Tasks 56 Chunks Type float32 numpy.ndarray

  • uo
    (time, zl, yh, xh)
    float32
    dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>

    long_name :

    Sea Water X Velocity

    units :

    m s-1

    cell_methods :

    zl:mean yh:mean xq:point time: mean

    time_avg_info :

    average_T1,average_T2,average_DT

    standard_name :

    sea_water_x_velocity

    interp_method :

    none

    Array Chunk Bytes 5.70 GiB 65.50 MiB Shape (480600, 65, 49, 1) (8760, 40, 49, 1) Count 5936 Tasks 112 Chunks Type float32 numpy.ndarray

  • Tflx_dia_diff
    (time, zi, yh, xh)
    float32
    dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>

    long_name :

    Diffusive diapycnal temperature flux across interfaces

    units :

    degC m s-1

    cell_methods :

    area:mean zi:point yh:mean xh:mean time: mean

    cell_measures :

    area: areacello

    time_avg_info :

    average_T1,average_T2,average_DT

    Array Chunk Bytes 5.79 GiB 65.50 MiB Shape (480600, 66, 49, 1) (8760, 40, 49, 1) Count 4592 Tasks 112 Chunks Type float32 numpy.ndarray

  • thetao
    (time, zl, yh, xh)
    float32
    dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>

    long_name :

    Sea Water Potential Temperature

    units :

    degC

    cell_methods :

    area:mean zl:mean yh:mean xh:mean time: mean

    cell_measures :

    volume: volcello area: areacello

    time_avg_info :

    average_T1,average_T2,average_DT

    standard_name :

    sea_water_potential_temperature

    Array Chunk Bytes 5.70 GiB 65.50 MiB Shape (480600, 65, 49, 1) (8760, 40, 49, 1) Count 4592 Tasks 112 Chunks Type float32 numpy.ndarray

  • KPP_buoyFlux
    (time, zi, yh, xh)
    float32
    dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>

    long_name :

    Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP

    units :

    m2/s3

    cell_methods :

    area:mean zi:point yh:mean xh:mean time: mean

    cell_measures :

    area: areacello

    time_avg_info :

    average_T1,average_T2,average_DT

    Array Chunk Bytes 5.79 GiB 65.50 MiB Shape (480600, 66, 49, 1) (8760, 40, 49, 1) Count 4592 Tasks 112 Chunks Type float32 numpy.ndarray

  • so
    (time, zl, yh, xh)
    float32
    dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>

    long_name :

    Sea Water Salinity

    units :

    psu

    cell_methods :

    area:mean zl:mean yh:mean xh:mean time: mean

    cell_measures :

    volume: volcello area: areacello

    time_avg_info :

    average_T1,average_T2,average_DT

    standard_name :

    sea_water_salinity

    Array Chunk Bytes 5.70 GiB 65.50 MiB Shape (480600, 65, 49, 1) (8760, 40, 49, 1) Count 4592 Tasks 112 Chunks Type float32 numpy.ndarray

  • zos
    (time, yh, xh)
    float32
    dask.array<chunksize=(8640, 49, 1), meta=np.ndarray>

    long_name :

    Sea surface height above geoid

    units :

    m

    cell_methods :

    area:mean yh:mean xh:mean time: mean

    cell_measures :

    area: areacello

    time_avg_info :

    average_T1,average_T2,average_DT

    standard_name :

    sea_surface_height_above_geoid

    Array Chunk Bytes 89.83 MiB 1.64 MiB Shape (480600, 49, 1) (8760, 49, 1) Count 2520 Tasks 56 Chunks Type float32 numpy.ndarray

  • KPP_OBLdepth
    (time, yh, xh)
    float32
    dask.array<chunksize=(8640, 49, 1), meta=np.ndarray>

    long_name :

    Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP

    units :

    meter

    cell_methods :

    area:mean yh:mean xh:mean time: mean

    cell_measures :

    area: areacello

    time_avg_info :

    average_T1,average_T2,average_DT

    Array Chunk Bytes 89.83 MiB 1.64 MiB Shape (480600, 49, 1) (8760, 49, 1) Count 2520 Tasks 56 Chunks Type float32 numpy.ndarray

  • vo
    (time, zl, yq, xh)
    float32
    dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>

    long_name :

    Sea Water Y Velocity

    units :

    m s-1

    cell_methods :

    zl:mean yq:point xh:mean time: mean

    time_avg_info :

    average_T1,average_T2,average_DT

    standard_name :

    sea_water_y_velocity

    interp_method :

    none

    Array Chunk Bytes 5.70 GiB 65.50 MiB Shape (480600, 65, 49, 1) (8760, 40, 49, 1) Count 4592 Tasks 112 Chunks Type float32 numpy.ndarray

  • average_DT
    (time)
    timedelta64[ns]
    dask.array<chunksize=(8640,), meta=np.ndarray>

    long_name :

    Length of average period

    Array Chunk Bytes 3.67 MiB 68.44 kiB Shape (480600,) (8760,) Count 168 Tasks 56 Chunks Type timedelta64[ns] numpy.ndarray

  • tauy
    (time, yq, xh)
    float32
    dask.array<chunksize=(8640, 49, 1), meta=np.ndarray>

    long_name :

    Meridional surface stress ocean interactions with atmos and ice

    units :

    Pa

    cell_methods :

    yq:point xh:mean time: mean

    time_avg_info :

    average_T1,average_T2,average_DT

    standard_name :

    surface_downward_y_stress

    interp_method :

    none

    Array Chunk Bytes 89.83 MiB 1.64 MiB Shape (480600, 49, 1) (8760, 49, 1) Count 2520 Tasks 56 Chunks Type float32 numpy.ndarray

  • dens
    (time, zl, yh, xh)
    float32
    dask.array<chunksize=(8640, 40, 49, 1), meta=np.ndarray>

    long_name :

    Sea Water Salinity

    units :

    kg/m^3

    cell_methods :

    area:mean zl:mean yh:mean xh:mean time: mean

    cell_measures :

    volume: volcello area: areacello

    time_avg_info :

    average_T1,average_T2,average_DT

    standard_name :

    sea_water_potential_density

    Array Chunk Bytes 5.70 GiB 65.50 MiB Shape (480600, 65, 49, 1) (8760, 40, 49, 1) Count 12432 Tasks 112 Chunks Type float32 numpy.ndarray

• Attributes: (0)

Save

mom6tao.chunk({"time": 24*365}).to_zarr(
    f"/glade/scratch/dcherian/archive/gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.001.mixpods/ocn/moorings/tao.zarr",
    mode="w",
    consolidated=True,
)

<xarray.backends.zarr.ZarrStore at 0x2b6dbf987d80>

Read sections

mom6tao = (
    xr.open_dataset(
        "/glade/scratch/dcherian/archive/gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.001.mixpods/ocn/moorings/tao.zarr",
        engine="zarr"
    )
    .drop_vars(["average_T1", "average_T2"])
    .chunk("auto")
)
mom6tao["time"] = mom6tao.indexes["time"].to_datetimeindex()

/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/coding/times.py:682: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
  dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/coding/times.py:360: FutureWarning: Index.ravel returning ndarray is deprecated; in a future version this will return a view on self.
  sample = dates.ravel()[0]
/glade/scratch/dcherian/tmp/ipykernel_21684/3129269274.py:5: RuntimeWarning: Converting a CFTimeIndex with dates from a non-standard calendar, 'noleap', to a pandas.DatetimeIndex, which uses dates from the standard calendar.  This may lead to subtle errors in operations that depend on the length of time between dates.
  mom6tao["time"] = mom6tao.indexes["time"].to_datetimeindex()

grid = xgcm.Grid(
    mom6tao,
    coords={"Z": {"outer": "zi", "center": "zl"}, 
            "X": {"center": "xh"},
            "Y": {"center": "yh", "left": "yq"},
           },
    periodic=False,
    boundary="fill",
    fill_value=np.nan,
    metrics = {("Z",): "h"}, 
)
grid

<xgcm.Grid>
Z Axis (not periodic, boundary='fill'):
  * outer    zi --> center
  * center   zl --> outer
X Axis (not periodic, boundary='fill'):
  * center   xh
Y Axis (not periodic, boundary='fill'):
  * center   yh --> left
  * left     yq --> center

mom6tao = mixpods.prepare(mom6tao, grid, monthly)
mom6tao

/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/cf_xarray/accessor.py:1638: UserWarning: Variables {'areacello'} not found in object but are referred to in the CF attributes.
  warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xgcm/grid.py:1471: UserWarning: Metric at ('time', 'zi', 'yh', 'xh') being interpolated from metrics at dimensions ('time', 'zl', 'yh', 'xh'). Boundary value set to 'extend'.
  warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xgcm/grid.py:1471: UserWarning: Metric at ('time', 'zi', 'yh', 'xh') being interpolated from metrics at dimensions ('time', 'zl', 'yh', 'xh'). Boundary value set to 'extend'.
  warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xgcm/grid.py:1471: UserWarning: Metric at ('time', 'zi', 'yq', 'xh') being interpolated from metrics at dimensions ('time', 'zl', 'yh', 'xh'). Boundary value set to 'extend'.
  warnings.warn(

<xarray.Dataset>
Dimensions:           (time: 480600, yh: 49, xh: 1, zi: 66, zl: 65, nv: 2,
                       yq: 49)
Coordinates:
  * nv                (nv) float64 1.0 2.0
  * time              (time) datetime64[ns] 1958-01-06T00:30:00 ... 2013-01-0...
  * xh                (xh) float64 -140.0
  * yh                (yh) float64 -5.911 -5.664 -5.417 ... 5.541 5.788 6.035
  * yq                (yq) float64 -6.035 -5.788 -5.541 ... 5.417 5.664 5.911
  * zi                (zi) float64 0.0 2.5 5.0 7.5 ... 5.503e+03 5.751e+03 6e+03
  * zl                (zl) float64 1.25 3.75 6.25 ... 5.627e+03 5.876e+03
    oni               (time) float32 nan nan nan nan nan ... nan nan nan nan nan
    warm_mask         (time) bool True True True True ... True True True True
    cool_mask         (time) bool False False False False ... False False False
    enso_transition   (time) <U12 '____________' ... '____________'
Data variables: (12/25)
    KPP_OBLdepth      (time, yh, xh) float32 dask.array<chunksize=(480600, 49, 1), meta=np.ndarray>
    KPP_buoyFlux      (time, zi, yh, xh) float32 dask.array<chunksize=(9612, 66, 49, 1), meta=np.ndarray>
    KPP_kheat         (time, zi, yh, xh) float32 dask.array<chunksize=(9612, 66, 49, 1), meta=np.ndarray>
    Kd_heat           (time, zi, yh, xh) float32 dask.array<chunksize=(9612, 66, 49, 1), meta=np.ndarray>
    Kv_u              (time, zl, yh, xh) float32 dask.array<chunksize=(9612, 65, 49, 1), meta=np.ndarray>
    SW                (time, yh, xh) float32 dask.array<chunksize=(480600, 49, 1), meta=np.ndarray>
    ...                ...
    zos               (time, yh, xh) float32 dask.array<chunksize=(480600, 49, 1), meta=np.ndarray>
    N2                (time, zi, yh, xh) float32 dask.array<chunksize=(9612, 1, 49, 1), meta=np.ndarray>
    S2                (time, zi, yh, xh) float32 dask.array<chunksize=(9612, 1, 1, 1), meta=np.ndarray>
    shred2            (time, zi, yh, xh) float32 dask.array<chunksize=(9612, 1, 1, 1), meta=np.ndarray>
    Ri                (time, zi, yh, xh) float32 dask.array<chunksize=(9612, 1, 1, 1), meta=np.ndarray>
    eucmax            (time, xh) float64 dask.array<chunksize=(9612, 1), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • time: 480600
  • yh: 49
  • xh: 1
  • zi: 66
  • zl: 65
  • nv: 2
  • yq: 49
• Coordinates: (11)
  • nv
    (nv)
    float64
    1.0 2.0

    cartesian_axis :

    N

    long_name :

    vertex number

    units :

    none

    array([1., 2.])

  • time
    (time)
    datetime64[ns]
    1958-01-06T00:30:00 ... 2013-01-...

    array(['1958-01-06T00:30:00.000000000', '1958-01-06T01:30:00.000000000',
           '1958-01-06T02:30:00.000000000', ..., '2013-01-05T21:30:00.000000000',
           '2013-01-05T22:30:00.000000000', '2013-01-05T23:30:00.000000000'],
          dtype='datetime64[ns]')

  • xh
    (xh)
    float64
    -140.0

    cartesian_axis :

    X

    domain_decomposition :

    [220, 222, 220, 221]

    long_name :

    h point nominal longitude

    units :

    degrees_east

    array([-140.])

  • yh
    (yh)
    float64
    -5.911 -5.664 ... 5.788 6.035

    cartesian_axis :

    Y

    domain_decomposition :

    [210, 258, 210, 221]

    long_name :

    h point nominal latitude

    units :

    degrees_north

    array([-5.911442, -5.664167, -5.416807, -5.16932 , -4.921678, -4.673864,
           -4.425866, -4.177682, -3.929311, -3.680757, -3.432028, -3.183131,
           -2.934077, -2.684877, -2.435542, -2.186084, -1.936516, -1.68685 ,
           -1.437098, -1.187273, -0.937388, -0.687456, -0.437489, -0.187499,
            0.0625  ,  0.312496,  0.562476,  0.812427,  1.062338,  1.312194,
            1.561984,  1.811694,  2.061313,  2.310828,  2.560226,  2.809495,
            3.058623,  3.3076  ,  3.556414,  3.805057,  4.05352 ,  4.301798,
            4.549888,  4.797793,  5.04552 ,  5.293081,  5.540501,  5.787812,
            6.035065])

  • yq
    (yq)
    float64
    -6.035 -5.788 ... 5.664 5.911

    cartesian_axis :

    Y

    domain_decomposition :

    [209, 257, 209, 221]

    long_name :

    q point nominal latitude

    units :

    degrees_north

    array([-6.035065, -5.787812, -5.540501, -5.293081, -5.04552 , -4.797793,
           -4.549888, -4.301798, -4.05352 , -3.805057, -3.556414, -3.3076  ,
           -3.058623, -2.809495, -2.560226, -2.310828, -2.061313, -1.811694,
           -1.561984, -1.312194, -1.062338, -0.812427, -0.562476, -0.312496,
           -0.0625  ,  0.187499,  0.437489,  0.687456,  0.937388,  1.187273,
            1.437098,  1.68685 ,  1.936516,  2.186084,  2.435542,  2.684877,
            2.934077,  3.183131,  3.432028,  3.680757,  3.929311,  4.177682,
            4.425866,  4.673864,  4.921678,  5.16932 ,  5.416807,  5.664167,
            5.911442])

  • zi
    (zi)
    float64
    0.0 2.5 5.0 ... 5.751e+03 6e+03

    cartesian_axis :

    Z

    long_name :

    Interface pseudo-depth, -z*

    positive :

    down

    units :

    meter

    array([0.00000e+00, 2.50000e+00, 5.00000e+00, 7.50000e+00, 1.00000e+01,
           1.27700e+01, 1.61500e+01, 2.01600e+01, 2.48100e+01, 3.01000e+01,
           3.60500e+01, 4.26600e+01, 4.99400e+01, 5.79100e+01, 6.65700e+01,
           7.59400e+01, 8.60200e+01, 9.68300e+01, 1.08370e+02, 1.20660e+02,
           1.33720e+02, 1.47570e+02, 1.62260e+02, 1.77850e+02, 1.94410e+02,
           2.12020e+02, 2.30780e+02, 2.50800e+02, 2.72220e+02, 2.95220e+02,
           3.19990e+02, 3.46780e+02, 3.75880e+02, 4.07640e+02, 4.42510e+02,
           4.81010e+02, 5.23800e+02, 5.71700e+02, 6.25710e+02, 6.87080e+02,
           7.57330e+02, 8.38280e+02, 9.32030e+02, 1.04083e+03, 1.16687e+03,
           1.31191e+03, 1.47672e+03, 1.66077e+03, 1.86211e+03, 2.07777e+03,
           2.30441e+03, 2.53891e+03, 2.77875e+03, 3.02206e+03, 3.26758e+03,
           3.51446e+03, 3.76218e+03, 4.01041e+03, 4.25895e+03, 4.50768e+03,
           4.75652e+03, 5.00516e+03, 5.25384e+03, 5.50255e+03, 5.75127e+03,
           6.00000e+03])

  • zl
    (zl)
    float64
    1.25 3.75 ... 5.627e+03 5.876e+03

    cartesian_axis :

    Z

    long_name :

    Layer pseudo-depth, -z*

    positive :

    down

    units :

    meter

    array([1.250000e+00, 3.750000e+00, 6.250000e+00, 8.750000e+00, 1.138500e+01,
           1.446000e+01, 1.815500e+01, 2.248500e+01, 2.745500e+01, 3.307500e+01,
           3.935500e+01, 4.630000e+01, 5.392500e+01, 6.224000e+01, 7.125500e+01,
           8.098000e+01, 9.142500e+01, 1.026000e+02, 1.145150e+02, 1.271900e+02,
           1.406450e+02, 1.549150e+02, 1.700550e+02, 1.861300e+02, 2.032150e+02,
           2.214000e+02, 2.407900e+02, 2.615100e+02, 2.837200e+02, 3.076050e+02,
           3.333850e+02, 3.613300e+02, 3.917600e+02, 4.250750e+02, 4.617600e+02,
           5.024050e+02, 5.477500e+02, 5.987050e+02, 6.563950e+02, 7.222050e+02,
           7.978050e+02, 8.851550e+02, 9.864300e+02, 1.103850e+03, 1.239390e+03,
           1.394315e+03, 1.568745e+03, 1.761440e+03, 1.969940e+03, 2.191090e+03,
           2.421660e+03, 2.658830e+03, 2.900405e+03, 3.144820e+03, 3.391020e+03,
           3.638320e+03, 3.886295e+03, 4.134680e+03, 4.383315e+03, 4.632100e+03,
           4.880840e+03, 5.129500e+03, 5.378195e+03, 5.626910e+03, 5.875635e+03])

  • oni
    (time)
    float32
    nan nan nan nan ... nan nan nan nan

    long_name :

    ONI

    units :

    degC

    cell_methods :

    area:mean yh:mean xh:mean time: mean

    cell_measures :

    area: areacello

    time_avg_info :

    average_T1,average_T2,average_DT

    standard_name :

    oceanic_nino_index

    array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)

  • warm_mask
    (time)
    bool
    True True True ... True True True

    array([ True,  True,  True, ...,  True,  True,  True])

  • cool_mask
    (time)
    bool
    False False False ... False False

    array([False, False, False, ..., False, False, False])

  • enso_transition
    (time)
    <U12
    '____________' ... '____________'

    long_name :

    ONI

    units :

    degC

    cell_methods :

    area:mean yh:mean xh:mean time: mean

    cell_measures :

    area: areacello

    time_avg_info :

    average_T1,average_T2,average_DT

    standard_name :

    oceanic_nino_index

    description :

    Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

    array(['____________', '____________', '____________', ...,
           '____________', '____________', '____________'], dtype='<U12')

• Data variables: (25)
  • KPP_OBLdepth
    (time, yh, xh)
    float32
    dask.array<chunksize=(480600, 49, 1), meta=np.ndarray>

    cell_measures :

    area: areacello

    cell_methods :

    area:mean yh:mean xh:mean time: mean

    long_name :

    Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    meter

    Array Chunk Bytes 89.83 MiB 89.83 MiB Shape (480600, 49, 1) (480600, 49, 1) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • KPP_buoyFlux
    (time, zi, yh, xh)
    float32
    dask.array<chunksize=(9612, 66, 49, 1), meta=np.ndarray>

    cell_measures :

    area: areacello

    cell_methods :

    area:mean zi:point yh:mean xh:mean time: mean

    long_name :

    Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    m2/s3

    Array Chunk Bytes 5.79 GiB 118.58 MiB Shape (480600, 66, 49, 1) (9612, 66, 49, 1) Count 51 Tasks 50 Chunks Type float32 numpy.ndarray

  • KPP_kheat
    (time, zi, yh, xh)
    float32
    dask.array<chunksize=(9612, 66, 49, 1), meta=np.ndarray>

    cell_measures :

    area: areacello

    cell_methods :

    area:mean zi:point yh:mean xh:mean time: mean

    long_name :

    Heat diffusivity due to KPP, as calculated by [CVMix] KPP

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    m2/s

    Array Chunk Bytes 5.79 GiB 118.58 MiB Shape (480600, 66, 49, 1) (9612, 66, 49, 1) Count 51 Tasks 50 Chunks Type float32 numpy.ndarray

  • Kd_heat
    (time, zi, yh, xh)
    float32
    dask.array<chunksize=(9612, 66, 49, 1), meta=np.ndarray>

    cell_measures :

    area: areacello

    cell_methods :

    area:mean zi:point yh:mean xh:mean time: mean

    long_name :

    Total diapycnal diffusivity for heat at interfaces

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    m2 s-1

    Array Chunk Bytes 5.79 GiB 118.58 MiB Shape (480600, 66, 49, 1) (9612, 66, 49, 1) Count 51 Tasks 50 Chunks Type float32 numpy.ndarray

  • Kv_u
    (time, zl, yh, xh)
    float32
    dask.array<chunksize=(9612, 65, 49, 1), meta=np.ndarray>

    cell_methods :

    zl:mean yh:mean xq:point time: mean

    interp_method :

    none

    long_name :

    Total vertical viscosity at u-points

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    m2 s-1

    Array Chunk Bytes 5.70 GiB 116.78 MiB Shape (480600, 65, 49, 1) (9612, 65, 49, 1) Count 51 Tasks 50 Chunks Type float32 numpy.ndarray

  • SW
    (time, yh, xh)
    float32
    dask.array<chunksize=(480600, 49, 1), meta=np.ndarray>

    cell_measures :

    area: areacello

    cell_methods :

    area:mean yh:mean xh:mean time: mean

    long_name :

    Shortwave radiation flux into ocean

    standard_name :

    net_downward_shortwave_flux_at_sea_water_surface

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    W m-2

    Array Chunk Bytes 89.83 MiB 89.83 MiB Shape (480600, 49, 1) (480600, 49, 1) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • Tflx_dia_diff
    (time, zi, yh, xh)
    float32
    dask.array<chunksize=(9612, 66, 49, 1), meta=np.ndarray>

    cell_measures :

    area: areacello

    cell_methods :

    area:mean zi:point yh:mean xh:mean time: mean

    long_name :

    Diffusive diapycnal temperature flux across interfaces

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    degC m s-1

    Array Chunk Bytes 5.79 GiB 118.58 MiB Shape (480600, 66, 49, 1) (9612, 66, 49, 1) Count 51 Tasks 50 Chunks Type float32 numpy.ndarray

  • average_DT
    (time)
    timedelta64[ns]
    dask.array<chunksize=(480600,), meta=np.ndarray>

    long_name :

    Length of average period

    Array Chunk Bytes 3.67 MiB 3.67 MiB Shape (480600,) (480600,) Count 2 Tasks 1 Chunks Type timedelta64[ns] numpy.ndarray

  • dens
    (time, zl, yh, xh)
    float32
    dask.array<chunksize=(9612, 65, 49, 1), meta=np.ndarray>

    cell_measures :

    volume: volcello area: areacello

    cell_methods :

    area:mean zl:mean yh:mean xh:mean time: mean

    long_name :

    Sea Water Salinity

    standard_name :

    sea_water_potential_density

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    kg/m^3

    Array Chunk Bytes 5.70 GiB 116.78 MiB Shape (480600, 65, 49, 1) (9612, 65, 49, 1) Count 51 Tasks 50 Chunks Type float32 numpy.ndarray

  • h
    (time, zl, yh, xh)
    float32
    dask.array<chunksize=(9612, 65, 49, 1), meta=np.ndarray>

    cell_measures :

    volume: volcello area: areacello

    cell_methods :

    area:mean zl:sum yh:mean xh:mean time: mean

    long_name :

    Layer Thickness

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    m

    Array Chunk Bytes 5.70 GiB 116.78 MiB Shape (480600, 65, 49, 1) (9612, 65, 49, 1) Count 51 Tasks 50 Chunks Type float32 numpy.ndarray

  • net_heat_surface
    (time, yh, xh)
    float32
    dask.array<chunksize=(480600, 49, 1), meta=np.ndarray>

    cell_measures :

    area: areacello

    cell_methods :

    area:mean yh:mean xh:mean time: mean

    long_name :

    Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments

    standard_name :

    surface_downward_heat_flux_in_sea_water

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    W m-2

    Array Chunk Bytes 89.83 MiB 89.83 MiB Shape (480600, 49, 1) (480600, 49, 1) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • so
    (time, zl, yh, xh)
    float32
    dask.array<chunksize=(9612, 65, 49, 1), meta=np.ndarray>

    cell_measures :

    volume: volcello area: areacello

    cell_methods :

    area:mean zl:mean yh:mean xh:mean time: mean

    long_name :

    Sea Water Salinity

    standard_name :

    sea_water_salinity

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    psu

    Array Chunk Bytes 5.70 GiB 116.78 MiB Shape (480600, 65, 49, 1) (9612, 65, 49, 1) Count 51 Tasks 50 Chunks Type float32 numpy.ndarray

  • taux
    (time, yh, xh)
    float32
    dask.array<chunksize=(480600, 49, 1), meta=np.ndarray>

    cell_methods :

    yh:mean xq:point time: mean

    interp_method :

    none

    long_name :

    Zonal surface stress from ocean interactions with atmos and ice

    standard_name :

    surface_downward_x_stress

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    Pa

    Array Chunk Bytes 89.83 MiB 89.83 MiB Shape (480600, 49, 1) (480600, 49, 1) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • tauy
    (time, yq, xh)
    float32
    dask.array<chunksize=(480600, 49, 1), meta=np.ndarray>

    cell_methods :

    yq:point xh:mean time: mean

    interp_method :

    none

    long_name :

    Meridional surface stress ocean interactions with atmos and ice

    standard_name :

    surface_downward_y_stress

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    Pa

    Array Chunk Bytes 89.83 MiB 89.83 MiB Shape (480600, 49, 1) (480600, 49, 1) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • thetao
    (time, zl, yh, xh)
    float32
    dask.array<chunksize=(9612, 65, 49, 1), meta=np.ndarray>

    cell_measures :

    volume: volcello area: areacello

    cell_methods :

    area:mean zl:mean yh:mean xh:mean time: mean

    long_name :

    Sea Water Potential Temperature

    standard_name :

    sea_water_potential_temperature

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    degC

    Array Chunk Bytes 5.70 GiB 116.78 MiB Shape (480600, 65, 49, 1) (9612, 65, 49, 1) Count 51 Tasks 50 Chunks Type float32 numpy.ndarray

  • time_bnds
    (time, nv)
    timedelta64[ns]
    dask.array<chunksize=(480600, 2), meta=np.ndarray>

    calendar :

    NOLEAP

    long_name :

    time axis boundaries

    Array Chunk Bytes 7.33 MiB 7.33 MiB Shape (480600, 2) (480600, 2) Count 2 Tasks 1 Chunks Type timedelta64[ns] numpy.ndarray

  • uo
    (time, zl, yh, xh)
    float32
    dask.array<chunksize=(9612, 65, 49, 1), meta=np.ndarray>

    cell_methods :

    zl:mean yh:mean xq:point time: mean

    interp_method :

    none

    long_name :

    Sea Water X Velocity

    standard_name :

    sea_water_x_velocity

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    m s-1

    Array Chunk Bytes 5.70 GiB 116.78 MiB Shape (480600, 65, 49, 1) (9612, 65, 49, 1) Count 51 Tasks 50 Chunks Type float32 numpy.ndarray

  • vo
    (time, zl, yq, xh)
    float32
    dask.array<chunksize=(9612, 65, 49, 1), meta=np.ndarray>

    cell_methods :

    zl:mean yq:point xh:mean time: mean

    interp_method :

    none

    long_name :

    Sea Water Y Velocity

    standard_name :

    sea_water_y_velocity

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    m s-1

    Array Chunk Bytes 5.70 GiB 116.78 MiB Shape (480600, 65, 49, 1) (9612, 65, 49, 1) Count 51 Tasks 50 Chunks Type float32 numpy.ndarray

  • volcello
    (time, zl, yh, xh)
    float32
    dask.array<chunksize=(9612, 65, 49, 1), meta=np.ndarray>

    cell_methods :

    area:sum zl:sum yh:sum xh:sum time: mean

    long_name :

    Ocean grid-cell volume

    standard_name :

    ocean_volume

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    m3

    Array Chunk Bytes 5.70 GiB 116.78 MiB Shape (480600, 65, 49, 1) (9612, 65, 49, 1) Count 51 Tasks 50 Chunks Type float32 numpy.ndarray

  • zos
    (time, yh, xh)
    float32
    dask.array<chunksize=(480600, 49, 1), meta=np.ndarray>

    cell_measures :

    area: areacello

    cell_methods :

    area:mean yh:mean xh:mean time: mean

    long_name :

    Sea surface height above geoid

    standard_name :

    sea_surface_height_above_geoid

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    m

    Array Chunk Bytes 89.83 MiB 89.83 MiB Shape (480600, 49, 1) (480600, 49, 1) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • N2
    (time, zi, yh, xh)
    float32
    dask.array<chunksize=(9612, 1, 49, 1), meta=np.ndarray>

    long_name :

    $N^2$

    units :

    s$^{-2}$

    Array Chunk Bytes 5.79 GiB 114.99 MiB Shape (480600, 66, 49, 1) (9612, 64, 49, 1) Count 3002 Tasks 150 Chunks Type float32 numpy.ndarray

  • S2
    (time, zi, yh, xh)
    float32
    dask.array<chunksize=(9612, 1, 1, 1), meta=np.ndarray>

    long_name :

    $S^2$

    units :

    s$^{-2}$

    Array Chunk Bytes 5.79 GiB 110.29 MiB Shape (480600, 66, 49, 1) (9612, 64, 47, 1) Count 11253 Tasks 450 Chunks Type float32 numpy.ndarray

  • shred2
    (time, zi, yh, xh)
    float32
    dask.array<chunksize=(9612, 1, 1, 1), meta=np.ndarray>

    long_name :

    $Sh_{red}^2$

    units :

    $s^{-2}$

    Array Chunk Bytes 5.79 GiB 110.29 MiB Shape (480600, 66, 49, 1) (9612, 64, 47, 1) Count 14554 Tasks 450 Chunks Type float32 numpy.ndarray

  • Ri
    (time, zi, yh, xh)
    float32
    dask.array<chunksize=(9612, 1, 1, 1), meta=np.ndarray>

    long_name :

    Ri

    units :

    Array Chunk Bytes 5.79 GiB 110.29 MiB Shape (480600, 66, 49, 1) (9612, 64, 47, 1) Count 14404 Tasks 450 Chunks Type float32 numpy.ndarray

  • eucmax
    (time, xh)
    float64
    dask.array<chunksize=(9612, 1), meta=np.ndarray>

    units :

    m

    positive :

    down

    Array Chunk Bytes 3.67 MiB 75.09 kiB Shape (480600, 1) (9612, 1) Count 803 Tasks 50 Chunks Type float64 numpy.ndarray

• Attributes: (0)

mom6140 = mom6tao.cf.sel(longitude=-140, latitude=0, method="nearest").cf.sel(Z=slice(150))
mom6140 = mom6140.update(
    {"n2s2pdf": mixpods.pdf_N2S2(mom6140[["S2", "N2", "eucmax"]])}
)
mom6140[["uo", "eucmax", "n2s2pdf", "S2", "N2"]].load()

<xarray.Dataset>
Dimensions:                (time: 480600, zl: 21, N2_bins: 29, S2_bins: 29,
                            enso_transition_phase: 7, zi: 22)
Coordinates: (12/14)
  * time                   (time) datetime64[ns] 1958-01-06T00:30:00 ... 2013...
    xh                     float64 -140.0
    yh                     float64 0.0625
    yq                     float64 -0.0625
  * zi                     (zi) float64 0.0 2.5 5.0 7.5 ... 120.7 133.7 147.6
  * zl                     (zl) float64 1.25 3.75 6.25 ... 114.5 127.2 140.6
    ...                     ...
    cool_mask              (time) bool False False False ... False False False
    enso_transition        (time) <U12 '____________' ... '____________'
  * N2_bins                (N2_bins) object (-5.0, -4.896551724137931] ... (-...
  * S2_bins                (S2_bins) object (-5.0, -4.896551724137931] ... (-...
  * enso_transition_phase  (enso_transition_phase) object 'none' ... 'all'
    bin_areas              (N2_bins, S2_bins) float64 0.0107 0.0107 ... 0.0107
Data variables:
    uo                     (time, zl) float32 -0.4169 -0.3548 ... 0.9186 0.8345
    eucmax                 (time) float64 114.5 114.5 114.5 ... 127.2 127.2
    n2s2pdf                (N2_bins, S2_bins, enso_transition_phase) float64 ...
    S2                     (time, zi) float32 nan 0.000642 ... 6.569e-05
    N2                     (time, zi) float32 nan 0.0001651 ... 0.0001154

xarray.Dataset

• Dimensions:
  • time: 480600
  • zl: 21
  • N2_bins: 29
  • S2_bins: 29
  • enso_transition_phase: 7
  • zi: 22
• Coordinates: (14)
  • time
    (time)
    datetime64[ns]
    1958-01-06T00:30:00 ... 2013-01-...

    array(['1958-01-06T00:30:00.000000000', '1958-01-06T01:30:00.000000000',
           '1958-01-06T02:30:00.000000000', ..., '2013-01-05T21:30:00.000000000',
           '2013-01-05T22:30:00.000000000', '2013-01-05T23:30:00.000000000'],
          dtype='datetime64[ns]')

  • xh
    ()
    float64
    -140.0

    cartesian_axis :

    X

    domain_decomposition :

    [220, 222, 220, 221]

    long_name :

    h point nominal longitude

    units :

    degrees_east

    array(-140.)

  • yh
    ()
    float64
    0.0625

    cartesian_axis :

    Y

    domain_decomposition :

    [210, 258, 210, 221]

    long_name :

    h point nominal latitude

    units :

    degrees_north

    array(0.06249997)

  • yq
    ()
    float64
    -0.0625

    cartesian_axis :

    Y

    domain_decomposition :

    [209, 257, 209, 221]

    long_name :

    q point nominal latitude

    units :

    degrees_north

    array(-0.06249997)

  • zi
    (zi)
    float64
    0.0 2.5 5.0 ... 120.7 133.7 147.6

    cartesian_axis :

    Z

    long_name :

    Interface pseudo-depth, -z*

    positive :

    down

    units :

    meter

    array([  0.  ,   2.5 ,   5.  ,   7.5 ,  10.  ,  12.77,  16.15,  20.16,  24.81,
            30.1 ,  36.05,  42.66,  49.94,  57.91,  66.57,  75.94,  86.02,  96.83,
           108.37, 120.66, 133.72, 147.57])

  • zl
    (zl)
    float64
    1.25 3.75 6.25 ... 127.2 140.6

    cartesian_axis :

    Z

    long_name :

    Layer pseudo-depth, -z*

    positive :

    down

    units :

    meter

    array([  1.25 ,   3.75 ,   6.25 ,   8.75 ,  11.385,  14.46 ,  18.155,  22.485,
            27.455,  33.075,  39.355,  46.3  ,  53.925,  62.24 ,  71.255,  80.98 ,
            91.425, 102.6  , 114.515, 127.19 , 140.645])

  • oni
    (time)
    float32
    nan nan nan nan ... nan nan nan nan

    long_name :

    ONI

    units :

    degC

    cell_methods :

    area:mean yh:mean xh:mean time: mean

    cell_measures :

    area: areacello

    time_avg_info :

    average_T1,average_T2,average_DT

    standard_name :

    oceanic_nino_index

    array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)

  • warm_mask
    (time)
    bool
    True True True ... True True True

    array([ True,  True,  True, ...,  True,  True,  True])

  • cool_mask
    (time)
    bool
    False False False ... False False

    array([False, False, False, ..., False, False, False])

  • enso_transition
    (time)
    <U12
    '____________' ... '____________'

    long_name :

    ONI

    units :

    degC

    cell_methods :

    area:mean yh:mean xh:mean time: mean

    cell_measures :

    area: areacello

    time_avg_info :

    average_T1,average_T2,average_DT

    standard_name :

    oceanic_nino_index

    description :

    Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

    array(['____________', '____________', '____________', ...,
           '____________', '____________', '____________'], dtype='<U12')

  • N2_bins
    (N2_bins)
    object
    (-5.0, -4.896551724137931] ... (...

    long_name :

    log$_{10} 4N^2$

    units :

    s$^{-2}$

    array([Interval(-5.0, -4.896551724137931, closed='right'),
           Interval(-4.896551724137931, -4.793103448275862, closed='right'),
           Interval(-4.793103448275862, -4.689655172413793, closed='right'),
           Interval(-4.689655172413793, -4.586206896551724, closed='right'),
           Interval(-4.586206896551724, -4.482758620689655, closed='right'),
           Interval(-4.482758620689655, -4.379310344827586, closed='right'),
           Interval(-4.379310344827586, -4.275862068965517, closed='right'),
           Interval(-4.275862068965517, -4.172413793103448, closed='right'),
           Interval(-4.172413793103448, -4.068965517241379, closed='right'),
           Interval(-4.068965517241379, -3.9655172413793105, closed='right'),
           Interval(-3.9655172413793105, -3.862068965517241, closed='right'),
           Interval(-3.862068965517241, -3.7586206896551726, closed='right'),
           Interval(-3.7586206896551726, -3.655172413793103, closed='right'),
           Interval(-3.655172413793103, -3.5517241379310347, closed='right'),
           Interval(-3.5517241379310347, -3.4482758620689653, closed='right'),
           Interval(-3.4482758620689653, -3.344827586206897, closed='right'),
           Interval(-3.344827586206897, -3.2413793103448274, closed='right'),
           Interval(-3.2413793103448274, -3.137931034482759, closed='right'),
           Interval(-3.137931034482759, -3.0344827586206895, closed='right'),
           Interval(-3.0344827586206895, -2.9310344827586206, closed='right'),
           Interval(-2.9310344827586206, -2.8275862068965516, closed='right'),
           Interval(-2.8275862068965516, -2.7241379310344827, closed='right'),
           Interval(-2.7241379310344827, -2.6206896551724137, closed='right'),
           Interval(-2.6206896551724137, -2.5172413793103448, closed='right'),
           Interval(-2.5172413793103448, -2.413793103448276, closed='right'),
           Interval(-2.413793103448276, -2.310344827586207, closed='right'),
           Interval(-2.310344827586207, -2.206896551724138, closed='right'),
           Interval(-2.206896551724138, -2.103448275862069, closed='right'),
           Interval(-2.103448275862069, -2.0, closed='right')], dtype=object)

  • S2_bins
    (S2_bins)
    object
    (-5.0, -4.896551724137931] ... (...

    long_name :

    log$_{10} S^2$

    units :

    s$^{-2}$

    array([Interval(-5.0, -4.896551724137931, closed='right'),
           Interval(-4.896551724137931, -4.793103448275862, closed='right'),
           Interval(-4.793103448275862, -4.689655172413793, closed='right'),
           Interval(-4.689655172413793, -4.586206896551724, closed='right'),
           Interval(-4.586206896551724, -4.482758620689655, closed='right'),
           Interval(-4.482758620689655, -4.379310344827586, closed='right'),
           Interval(-4.379310344827586, -4.275862068965517, closed='right'),
           Interval(-4.275862068965517, -4.172413793103448, closed='right'),
           Interval(-4.172413793103448, -4.068965517241379, closed='right'),
           Interval(-4.068965517241379, -3.9655172413793105, closed='right'),
           Interval(-3.9655172413793105, -3.862068965517241, closed='right'),
           Interval(-3.862068965517241, -3.7586206896551726, closed='right'),
           Interval(-3.7586206896551726, -3.655172413793103, closed='right'),
           Interval(-3.655172413793103, -3.5517241379310347, closed='right'),
           Interval(-3.5517241379310347, -3.4482758620689653, closed='right'),
           Interval(-3.4482758620689653, -3.344827586206897, closed='right'),
           Interval(-3.344827586206897, -3.2413793103448274, closed='right'),
           Interval(-3.2413793103448274, -3.137931034482759, closed='right'),
           Interval(-3.137931034482759, -3.0344827586206895, closed='right'),
           Interval(-3.0344827586206895, -2.9310344827586206, closed='right'),
           Interval(-2.9310344827586206, -2.8275862068965516, closed='right'),
           Interval(-2.8275862068965516, -2.7241379310344827, closed='right'),
           Interval(-2.7241379310344827, -2.6206896551724137, closed='right'),
           Interval(-2.6206896551724137, -2.5172413793103448, closed='right'),
           Interval(-2.5172413793103448, -2.413793103448276, closed='right'),
           Interval(-2.413793103448276, -2.310344827586207, closed='right'),
           Interval(-2.310344827586207, -2.206896551724138, closed='right'),
           Interval(-2.206896551724138, -2.103448275862069, closed='right'),
           Interval(-2.103448275862069, -2.0, closed='right')], dtype=object)

  • enso_transition_phase
    (enso_transition_phase)
    object
    'none' 'El-Nino cool' ... 'all'

    description :

    El-Nino transition phase as defined by Warner and Moum (2019). 'none' uses all available data. 'all' sums over all ENSO transition phases

    array(['none', 'El-Nino cool', 'El-Nino warm', 'La-Nina cool', 'La-Nina warm',
           '____________', 'all'], dtype=object)

  • bin_areas
    (N2_bins, S2_bins)
    float64
    0.0107 0.0107 ... 0.0107 0.0107

    long_name :

    log$_{10} 4N^2$

    units :

    s$^{-2}$

    array([[0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155],
           [0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155],
           [0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155],
           [0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
    ...
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155],
           [0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155],
           [0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155],
           [0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155, 0.01070155,
            0.01070155, 0.01070155, 0.01070155, 0.01070155]])

• Data variables: (5)
  • uo
    (time, zl)
    float32
    -0.4169 -0.3548 ... 0.9186 0.8345

    cell_methods :

    zl:mean yh:mean xq:point time: mean

    interp_method :

    none

    long_name :

    Sea Water X Velocity

    standard_name :

    sea_water_x_velocity

    time_avg_info :

    average_T1,average_T2,average_DT

    units :

    m s-1

    array([[-0.41687828, -0.3548001 , -0.29051697, ...,  0.19936964,
             0.1963826 ,  0.16460362],
           [-0.42789847, -0.36919075, -0.28704643, ...,  0.20665647,
             0.20315903,  0.17001963],
           [-0.41281044, -0.37920535, -0.31642604, ...,  0.21360898,
             0.20957631,  0.1749618 ],
           ...,
           [-0.5487939 , -0.49319744, -0.46427798, ...,  0.91487074,
             0.9171482 ,  0.8338859 ],
           [-0.5719254 , -0.5104333 , -0.4784357 , ...,  0.9157798 ,
             0.91788685,  0.83420825],
           [-0.58580995, -0.52609766, -0.49354792, ...,  0.91667324,
             0.91859317,  0.8344977 ]], dtype=float32)

  • eucmax
    (time)
    float64
    114.5 114.5 114.5 ... 127.2 127.2

    units :

    m

    positive :

    down

    array([114.515, 114.515, 114.515, ..., 127.19 , 127.19 , 127.19 ])

  • n2s2pdf
    (N2_bins, S2_bins, enso_transition_phase)
    float64
    0.1331 0.1194 0.1213 ... 0.0 0.0

    long_name :

    $P(S^2, 4N^2)$

    array([[[1.33083402e-01, 1.19440960e-01, 1.21286220e-01, ...,
             1.16637644e-01, 1.35906130e-01, 1.30297206e-01],
            [9.47084187e-02, 9.21892709e-02, 9.36881582e-02, ...,
             8.40347057e-02, 9.34372838e-02, 9.59631024e-02],
            [6.31582248e-02, 5.85543039e-02, 5.95537129e-02, ...,
             5.82556381e-02, 6.29901191e-02, 6.33241550e-02],
            ...,
            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 0.00000000e+00]],
    
           [[2.16614782e-01, 1.96040301e-01, 2.22340778e-01, ...,
             2.08759900e-01, 2.13945183e-01, 2.19249831e-01],
            [1.39908192e-01, 1.28402100e-01, 1.51581837e-01, ...,
             1.20302315e-01, 1.37274215e-01, 1.42508080e-01],
            [9.43903140e-02, 8.59287479e-02, 1.01469567e-01, ...,
             8.52983854e-02, 9.13123861e-02, 9.74284072e-02],
    ...
            [5.78372022e-05, 4.91021417e-04, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 1.14925871e-04],
            [1.44593006e-05, 1.22755354e-04, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 2.87314678e-05],
            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 0.00000000e+00]],
    
           [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
            ...,
            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
            [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
             0.00000000e+00, 0.00000000e+00, 0.00000000e+00]]])

  • S2
    (time, zi)
    float32
    nan 0.000642 ... 6.569e-05

    long_name :

    $S^2$

    units :

    s$^{-2}$

    array([[           nan, 6.42026193e-04, 6.69850677e-04, ...,
            8.53405311e-07, 1.35371965e-05, 2.78707794e-05],
           [           nan, 5.97898150e-04, 1.08378567e-03, ...,
            9.74680461e-07, 1.44629284e-05, 2.93617195e-05],
           [           nan, 1.96984271e-04, 6.54170639e-04, ...,
            1.11925499e-06, 1.54869267e-05, 3.08874623e-05],
           ...,
           [           nan, 5.05985576e-04, 1.36902774e-04, ...,
            3.91296766e-07, 4.17713163e-05, 6.58464851e-05],
           [           nan, 6.18631719e-04, 1.67522972e-04, ...,
            4.06962386e-07, 4.21503319e-05, 6.57668061e-05],
           [           nan, 5.82929235e-04, 1.73216948e-04, ...,
            4.23976729e-07, 4.25377802e-05, 6.56892807e-05]], dtype=float32)

  • N2
    (time, zi)
    float32
    nan 0.0001651 ... 0.0001154

    long_name :

    $N^2$

    units :

    s$^{-2}$

    array([[          nan, 1.6513749e-04, 1.7942801e-04, ..., 3.2384944e-04,
            4.1603090e-04, 3.4569020e-04],
           [          nan, 1.2988922e-04, 2.2807912e-04, ..., 3.2151298e-04,
            4.1487470e-04, 3.4786059e-04],
           [          nan, 5.9564973e-05, 1.6756463e-04, ..., 3.1945881e-04,
            4.1398944e-04, 3.4971270e-04],
           ...,
           [          nan, 4.2016585e-05, 2.5011437e-05, ..., 1.3433944e-04,
            3.4483190e-04, 1.1816219e-04],
           [          nan, 5.8811216e-05, 3.4466822e-05, ..., 1.3451460e-04,
            3.4506290e-04, 1.1684291e-04],
           [          nan, 5.8607242e-05, 3.6844129e-05, ..., 1.3478179e-04,
            3.4503444e-04, 1.1544217e-04]], dtype=float32)

• Attributes: (0)

mom6140.uo.cf.plot(robust=True)
mom6140.eucmax.cf.plot()

[<matplotlib.lines.Line2D at 0x2b153c1c66e0>]

mixpods.plot_n2s2pdf(mom6140.n2s2pdf.sel(enso_transition_phase="none"))

<matplotlib.contour.QuadContourSet at 0x2b152f7a38e0>

Pick simulations

datasets = {"TAO": tao_gridded, "MITgcm": mitgcm, "MOM6": mom6140}

Compare EUC maximum

mixpods.plot_eucmax_timeseries(datasets, obs="TAO")

Mean profiles

def plot_profile_fill(da, label):
    import hvplot.pandas
    
    Zname = da.cf.axes["Z"][0]
    da = da.copy(deep=True)
    da[Zname] = normalize_z(da[Zname])
    mean = da.mean("time")
    std = da.std("time")
    
    df_ = xr.Dataset({"low": mean-std, "high": mean + std}).reset_coords(drop=True).to_dataframe()
    return (
        df_.hvplot.area(x=Zname, y="low", y2 = "high", hover=False).opts(alpha=0.5)
        * mean.hvplot.line(label=label)
    )

def cfplot(da, label):
    Zname = da.cf.axes["Z"][0]
    da = da.load().copy(deep=True)
    da[Zname] = normalize_z(da[Zname])
    return da.hvplot.line(label=label)


kwargs = dict(show_grid=True, invert_axes=True, legend_position="right", frame_width=300, frame_height=500, 
              ylim=(-1e-3, 1.9e-3), xlim=(-250, 0))

def map_hvplot(func, datasets):
    return reduce(
    operator.mul,
    (func(ds.load(), name) for name, ds in datasets.items())
)

S2 = map_hvplot(lambda ds, name: plot_profile_fill(ds.S2.load(), name), datasets).opts(**kwargs, ylabel="S^2")
N2 = map_hvplot(lambda ds, name: plot_profile_fill(4 * ds.N2.load(), name), datasets).opts(**kwargs, ylabel="4N^2")
Ri = (
    map_hvplot(lambda ds, name: cfplot(ds.Ri.median("time"), name), datasets)
    * hv.HLine(0.25).opts(color='k', line_width=0.5)
).opts(**kwargs, ylabel="median Ri").opts(ylim=(0,1))

(S2 + N2 + Ri)

Remap to EUC coordinate

gcmeq.coords["zeuc"] = gcmeq.depth - gcmeq.eucmax
remapped = flox.xarray.xarray_reduce(
    gcmeq.drop_vars(["SSH", "KPPhbl", "mld", "eucmax"], errors="ignore"),
    "zeuc",
    dim="depth",
    func="mean",
    expected_groups=(np.arange(-250, 250.1, 5),),
    isbin=(True,),
)
remapped["zeuc_bins"].attrs["units"] = "m"
remapped

<xarray.Dataset>
Dimensions:          (time: 174000, longitude: 4, zeuc_bins: 100)
Coordinates:
    latitude         float64 0.025
  * longitude        (longitude) float64 -155.0 -140.0 -125.0 -110.0
  * time             (time) datetime64[ns] 1998-12-31T18:00:00 ... 2018-11-06...
    cool_mask        (time) bool True True True True ... False False False False
    warm_mask        (time) bool False False False False ... True True True True
  * zeuc_bins        (zeuc_bins) object (-250.0, -245.0] ... (245.0, 250.0]
Data variables: (12/25)
    DFrI_TH          (time, longitude, zeuc_bins) float32 dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
    KPPdiffKzT       (time, longitude, zeuc_bins) float32 dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
    KPPg_TH          (time, longitude, zeuc_bins) float32 dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
    KPPviscAz        (time, longitude, zeuc_bins) float32 dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
    VISrI_Um         (time, longitude, zeuc_bins) float32 dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
    VISrI_Vm         (time, longitude, zeuc_bins) float32 dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
    ...               ...
    S2               (time, longitude, zeuc_bins) float32 dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
    shear            (time, longitude, zeuc_bins) float32 dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
    N2               (time, longitude, zeuc_bins) float32 dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
    shred2           (time, longitude, zeuc_bins) float32 dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
    Ri               (time, longitude, zeuc_bins) float32 dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>
    enso_transition  (zeuc_bins, time) <U12 'La-Nina cool' ... 'El-Nino warm'
Attributes:
    easting:   longitude
    northing:  latitude
    title:     Station profile, index (i,j)=(1201,240)

xarray.Dataset

• Dimensions:
  • time: 174000
  • longitude: 4
  • zeuc_bins: 100
• Coordinates: (6)
  • latitude
    ()
    float64
    0.025

    array(0.025)

  • longitude
    (longitude)
    float64
    -155.0 -140.0 -125.0 -110.0

    array([-155.024994, -140.024994, -125.025002, -110.025002])

  • time
    (time)
    datetime64[ns]
    1998-12-31T18:00:00 ... 2018-11-...

    array(['1998-12-31T18:00:00.000000000', '1998-12-31T19:00:00.000000000',
           '1998-12-31T20:00:00.000000000', ..., '2018-11-06T15:00:32.000000000',
           '2018-11-06T16:00:16.000000000', '2018-11-06T17:00:00.000000000'],
          dtype='datetime64[ns]')

  • cool_mask
    (time)
    bool
    True True True ... False False

    array([ True,  True,  True, ..., False, False, False])

  • warm_mask
    (time)
    bool
    False False False ... True True

    array([False, False, False, ...,  True,  True,  True])

  • zeuc_bins
    (zeuc_bins)
    object
    (-250.0, -245.0] ... (245.0, 250.0]

    axis :

    Z

    units :

    m

    array([Interval(-250.0, -245.0, closed='right'),
           Interval(-245.0, -240.0, closed='right'),
           Interval(-240.0, -235.0, closed='right'),
           Interval(-235.0, -230.0, closed='right'),
           Interval(-230.0, -225.0, closed='right'),
           Interval(-225.0, -220.0, closed='right'),
           Interval(-220.0, -215.0, closed='right'),
           Interval(-215.0, -210.0, closed='right'),
           Interval(-210.0, -205.0, closed='right'),
           Interval(-205.0, -200.0, closed='right'),
           Interval(-200.0, -195.0, closed='right'),
           Interval(-195.0, -190.0, closed='right'),
           Interval(-190.0, -185.0, closed='right'),
           Interval(-185.0, -180.0, closed='right'),
           Interval(-180.0, -175.0, closed='right'),
           Interval(-175.0, -170.0, closed='right'),
           Interval(-170.0, -165.0, closed='right'),
           Interval(-165.0, -160.0, closed='right'),
           Interval(-160.0, -155.0, closed='right'),
           Interval(-155.0, -150.0, closed='right'),
           Interval(-150.0, -145.0, closed='right'),
           Interval(-145.0, -140.0, closed='right'),
           Interval(-140.0, -135.0, closed='right'),
           Interval(-135.0, -130.0, closed='right'),
           Interval(-130.0, -125.0, closed='right'),
           Interval(-125.0, -120.0, closed='right'),
           Interval(-120.0, -115.0, closed='right'),
           Interval(-115.0, -110.0, closed='right'),
           Interval(-110.0, -105.0, closed='right'),
           Interval(-105.0, -100.0, closed='right'),
           Interval(-100.0, -95.0, closed='right'),
           Interval(-95.0, -90.0, closed='right'),
           Interval(-90.0, -85.0, closed='right'),
           Interval(-85.0, -80.0, closed='right'),
           Interval(-80.0, -75.0, closed='right'),
           Interval(-75.0, -70.0, closed='right'),
           Interval(-70.0, -65.0, closed='right'),
           Interval(-65.0, -60.0, closed='right'),
           Interval(-60.0, -55.0, closed='right'),
           Interval(-55.0, -50.0, closed='right'),
           Interval(-50.0, -45.0, closed='right'),
           Interval(-45.0, -40.0, closed='right'),
           Interval(-40.0, -35.0, closed='right'),
           Interval(-35.0, -30.0, closed='right'),
           Interval(-30.0, -25.0, closed='right'),
           Interval(-25.0, -20.0, closed='right'),
           Interval(-20.0, -15.0, closed='right'),
           Interval(-15.0, -10.0, closed='right'),
           Interval(-10.0, -5.0, closed='right'),
           Interval(-5.0, 0.0, closed='right'), Interval(0.0, 5.0, closed='right'),
           Interval(5.0, 10.0, closed='right'),
           Interval(10.0, 15.0, closed='right'),
           Interval(15.0, 20.0, closed='right'),
           Interval(20.0, 25.0, closed='right'),
           Interval(25.0, 30.0, closed='right'),
           Interval(30.0, 35.0, closed='right'),
           Interval(35.0, 40.0, closed='right'),
           Interval(40.0, 45.0, closed='right'),
           Interval(45.0, 50.0, closed='right'),
           Interval(50.0, 55.0, closed='right'),
           Interval(55.0, 60.0, closed='right'),
           Interval(60.0, 65.0, closed='right'),
           Interval(65.0, 70.0, closed='right'),
           Interval(70.0, 75.0, closed='right'),
           Interval(75.0, 80.0, closed='right'),
           Interval(80.0, 85.0, closed='right'),
           Interval(85.0, 90.0, closed='right'),
           Interval(90.0, 95.0, closed='right'),
           Interval(95.0, 100.0, closed='right'),
           Interval(100.0, 105.0, closed='right'),
           Interval(105.0, 110.0, closed='right'),
           Interval(110.0, 115.0, closed='right'),
           Interval(115.0, 120.0, closed='right'),
           Interval(120.0, 125.0, closed='right'),
           Interval(125.0, 130.0, closed='right'),
           Interval(130.0, 135.0, closed='right'),
           Interval(135.0, 140.0, closed='right'),
           Interval(140.0, 145.0, closed='right'),
           Interval(145.0, 150.0, closed='right'),
           Interval(150.0, 155.0, closed='right'),
           Interval(155.0, 160.0, closed='right'),
           Interval(160.0, 165.0, closed='right'),
           Interval(165.0, 170.0, closed='right'),
           Interval(170.0, 175.0, closed='right'),
           Interval(175.0, 180.0, closed='right'),
           Interval(180.0, 185.0, closed='right'),
           Interval(185.0, 190.0, closed='right'),
           Interval(190.0, 195.0, closed='right'),
           Interval(195.0, 200.0, closed='right'),
           Interval(200.0, 205.0, closed='right'),
           Interval(205.0, 210.0, closed='right'),
           Interval(210.0, 215.0, closed='right'),
           Interval(215.0, 220.0, closed='right'),
           Interval(220.0, 225.0, closed='right'),
           Interval(225.0, 230.0, closed='right'),
           Interval(230.0, 235.0, closed='right'),
           Interval(235.0, 240.0, closed='right'),
           Interval(240.0, 245.0, closed='right'),
           Interval(245.0, 250.0, closed='right')], dtype=object)

• Data variables: (25)
  • DFrI_TH
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 9425 Tasks 116 Chunks Type float32 numpy.ndarray

  • KPPdiffKzT
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 9425 Tasks 116 Chunks Type float32 numpy.ndarray

  • KPPg_TH
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 22301 Tasks 116 Chunks Type float32 numpy.ndarray

  • KPPviscAz
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 9425 Tasks 116 Chunks Type float32 numpy.ndarray

  • VISrI_Um
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 9425 Tasks 116 Chunks Type float32 numpy.ndarray

  • VISrI_Vm
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 9425 Tasks 116 Chunks Type float32 numpy.ndarray

  • salt
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 9425 Tasks 116 Chunks Type float32 numpy.ndarray

  • theta
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 9425 Tasks 116 Chunks Type float32 numpy.ndarray

  • u
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 9425 Tasks 116 Chunks Type float32 numpy.ndarray

  • v
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 9425 Tasks 116 Chunks Type float32 numpy.ndarray

  • w
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 9425 Tasks 116 Chunks Type float32 numpy.ndarray

  • Jq_shear
    (time, longitude, zeuc_bins)
    float64
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 531.01 MiB 4.58 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 31190 Tasks 116 Chunks Type float64 numpy.ndarray

  • nonlocal_flux
    (time, longitude, zeuc_bins)
    float64
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 531.01 MiB 4.58 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 31190 Tasks 116 Chunks Type float64 numpy.ndarray

  • dens
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 30915 Tasks 116 Chunks Type float32 numpy.ndarray

  • Jq
    (time, longitude, zeuc_bins)
    float64
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 531.01 MiB 4.58 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 65555 Tasks 116 Chunks Type float64 numpy.ndarray

  • dJdz
    (time, longitude, zeuc_bins)
    float64
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 531.01 MiB 4.58 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 91307 Tasks 116 Chunks Type float64 numpy.ndarray

  • dTdt
    (time, longitude, zeuc_bins)
    float64
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    long_name :

    $∂T/∂t = -1/(ρ_0c_p) ∂J_q/∂z$

    units :

    °C/month

    Array Chunk Bytes 531.01 MiB 4.58 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 112767 Tasks 116 Chunks Type float64 numpy.ndarray

  • uz
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 10121 Tasks 116 Chunks Type float32 numpy.ndarray

  • vz
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 10121 Tasks 116 Chunks Type float32 numpy.ndarray

  • S2
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    long_name :

    $S^2$

    units :

    s$^{-2}$

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 20010 Tasks 116 Chunks Type float32 numpy.ndarray

  • shear
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    long_name :

    |$u_z$|

    units :

    s$^{-1}$

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 20126 Tasks 116 Chunks Type float32 numpy.ndarray

  • N2
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    long_name :

    $N^2$

    units :

    s$^{-2}$

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 31727 Tasks 116 Chunks Type float32 numpy.ndarray

  • shred2
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    long_name :

    Reduced shear$^2$

    units :

    $s^{-2}$

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 51389 Tasks 116 Chunks Type float32 numpy.ndarray

  • Ri
    (time, longitude, zeuc_bins)
    float32
    dask.array<chunksize=(6000, 1, 100), meta=np.ndarray>

    long_name :

    Ri

    units :

    Array Chunk Bytes 265.50 MiB 2.29 MiB Shape (174000, 4, 100) (6000, 1, 100) Count 51273 Tasks 116 Chunks Type float32 numpy.ndarray

  • enso_transition
    (zeuc_bins, time)
    <U12
    'La-Nina cool' ... 'El-Nino warm'

    description :

    Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

    array([['La-Nina cool', 'La-Nina cool', 'La-Nina cool', ...,
            'El-Nino warm', 'El-Nino warm', 'El-Nino warm'],
           ['La-Nina cool', 'La-Nina cool', 'La-Nina cool', ...,
            'El-Nino warm', 'El-Nino warm', 'El-Nino warm'],
           ['La-Nina cool', 'La-Nina cool', 'La-Nina cool', ...,
            'El-Nino warm', 'El-Nino warm', 'El-Nino warm'],
           ...,
           ['La-Nina cool', 'La-Nina cool', 'La-Nina cool', ...,
            'El-Nino warm', 'El-Nino warm', 'El-Nino warm'],
           ['La-Nina cool', 'La-Nina cool', 'La-Nina cool', ...,
            'El-Nino warm', 'El-Nino warm', 'El-Nino warm'],
           ['La-Nina cool', 'La-Nina cool', 'La-Nina cool', ...,
            'El-Nino warm', 'El-Nino warm', 'El-Nino warm']], dtype='<U12')

• Attributes: (3)

  easting :

  longitude

  northing :

  latitude

  title :

  Station profile, index (i,j)=(1201,240)

remapped = remapped.persist()

cluster.scale(6)

Seasonal median Ri: (0, 140)

remapped["longitude"] = [-155.0, -140, -125, -110]

fg = (
    remapped.Ri.groupby("time.season").median()
    .sel(season=["DJF", "MAM", "JJA", "SON"], longitude=[-140, -110])
    .cf.plot(col="season", row="longitude", xlim=(0,1.5), ylim=(-20, None), label="MITgcm")
)
fg.data = tao_Ri.cf.sel(quantile=0.5, longitude=[-140, -110])
fg.map_dataarray_line(xr.plot.line, x=None, y="zeuc", hue="season", color='k', label="TAO")
fg.map(lambda: plt.axvline(0.25))
fg.axes[-1, -1].legend(bbox_to_anchor=(1.5, 1))

<matplotlib.legend.Legend at 0x2add93b58220>

Stability diagram: 4N2-S2 plane

Warner & Moum (2019):

Both velocity and temperature are hourly averaged before interpolation to 5-m vertical bins

Contours enclose 50% of data

mixpods.plot_stability_diagram_by_dataset(datasets)

Questions:

1. internal wave spectrum?

2. minor axis is smaller for model, particularly MITgcm-> too diffusive?

3. Can we think of this as the model relaxing to critical Ri too quickly

mixpods.plot_stability_diagram_by_phase(datasets, obs="TAO", fig=None)

Composed

fig = plt.figure(constrained_layout=True, figsize=(12, 6))
subfigs = fig.subfigures(2, 1, height_ratios=[1.2, 1])
top = subfigs[0].subfigures(1, 3, width_ratios=[1, 5, 1])

mixpods.plot_stability_diagram_by_dataset(datasets, fig=top[1])
mixpods.plot_stability_diagram_by_phase(datasets, fig=subfigs[1])

(   
    tao_gridded.n2s2pdf
    .sel(enso_transition_phase=["La-Nina cool", "La-Nina warm", "El-Nino warm", "El-Nino cool"])
    .sum("N2_bins")
    .plot(hue="enso_transition_phase")
)
plt.figure()
(
    tao_gridded.n2s2pdf
    .sel(enso_transition_phase=["La-Nina cool", "La-Nina warm", "El-Nino warm", "El-Nino cool"])
    .sum("S2_bins")
    .plot(hue="enso_transition_phase")
)

[<matplotlib.lines.Line2D at 0x2b36e2bc2ad0>,
 <matplotlib.lines.Line2D at 0x2b36e2bc32e0>,
 <matplotlib.lines.Line2D at 0x2b36e2bc3310>,
 <matplotlib.lines.Line2D at 0x2b36e0ca52d0>]

oni = pump.obs.process_oni().sel(time=slice("2005-Oct", "2015"))
(
    oni.hvplot.step(color='k')
+ pump.obs.make_enso_transition_mask().sel(time=slice("2005-Jun", "2015")).reset_coords(drop=True).hvplot.step()
).cols(1)

Seasonal mean heat flux

remapped.Jq.load()

<xarray.DataArray 'Jq' (time: 174000, zeuc: 100)>
array([[        nan,         nan, -0.07841657, ...,         nan,
                nan,         nan],
       [        nan,         nan, -0.07973828, ...,         nan,
                nan,         nan],
       [        nan,         nan, -0.08094554, ...,         nan,
                nan,         nan],
       ...,
       [        nan, -0.07447129,         nan, ...,         nan,
                nan,         nan],
       [        nan, -0.07471326,         nan, ...,         nan,
                nan,         nan],
       [        nan, -0.07509062,         nan, ...,         nan,
                nan,         nan]])
Coordinates:
    latitude   float64 0.025
    longitude  float64 -140.0
  * time       (time) datetime64[ns] 1998-12-31T18:00:00 ... 2018-11-06T17:00:00
  * zeuc       (zeuc) float64 -247.5 -242.5 -237.5 -232.5 ... 237.5 242.5 247.5

xarray.DataArray

'Jq'

• time: 174000
• zeuc: 100

• nan nan -0.07842 nan -0.06425 nan -0.05399 ... nan nan nan nan nan nan

  array([[        nan,         nan, -0.07841657, ...,         nan,
                  nan,         nan],
         [        nan,         nan, -0.07973828, ...,         nan,
                  nan,         nan],
         [        nan,         nan, -0.08094554, ...,         nan,
                  nan,         nan],
         ...,
         [        nan, -0.07447129,         nan, ...,         nan,
                  nan,         nan],
         [        nan, -0.07471326,         nan, ...,         nan,
                  nan,         nan],
         [        nan, -0.07509062,         nan, ...,         nan,
                  nan,         nan]])

• Coordinates: (4)
  • latitude
    ()
    float64
    0.025

    array(0.025)

  • longitude
    ()
    float64
    -140.0

    array(-140.0249939)

  • time
    (time)
    datetime64[ns]
    1998-12-31T18:00:00 ... 2018-11-...

    array(['1998-12-31T18:00:00.000000000', '1998-12-31T19:00:00.000000000',
           '1998-12-31T20:00:00.000000000', ..., '2018-11-06T15:00:32.000000000',
           '2018-11-06T16:00:16.000000000', '2018-11-06T17:00:00.000000000'],
          dtype='datetime64[ns]')

  • zeuc
    (zeuc)
    float64
    -247.5 -242.5 ... 242.5 247.5

    axis :

    Z

    units :

    m

    array([-247.5, -242.5, -237.5, -232.5, -227.5, -222.5, -217.5, -212.5, -207.5,
           -202.5, -197.5, -192.5, -187.5, -182.5, -177.5, -172.5, -167.5, -162.5,
           -157.5, -152.5, -147.5, -142.5, -137.5, -132.5, -127.5, -122.5, -117.5,
           -112.5, -107.5, -102.5,  -97.5,  -92.5,  -87.5,  -82.5,  -77.5,  -72.5,
            -67.5,  -62.5,  -57.5,  -52.5,  -47.5,  -42.5,  -37.5,  -32.5,  -27.5,
            -22.5,  -17.5,  -12.5,   -7.5,   -2.5,    2.5,    7.5,   12.5,   17.5,
             22.5,   27.5,   32.5,   37.5,   42.5,   47.5,   52.5,   57.5,   62.5,
             67.5,   72.5,   77.5,   82.5,   87.5,   92.5,   97.5,  102.5,  107.5,
            112.5,  117.5,  122.5,  127.5,  132.5,  137.5,  142.5,  147.5,  152.5,
            157.5,  162.5,  167.5,  172.5,  177.5,  182.5,  187.5,  192.5,  197.5,
            202.5,  207.5,  212.5,  217.5,  222.5,  227.5,  232.5,  237.5,  242.5,
            247.5])

• Attributes: (0)

import hvplot.xarray

(
    remapped.Jq.groupby("time.season").mean()
    .reindex(season=["DJF", "MAM", "JJA", "SON"])
    .cf.plot.line(col="season")
)

<xarray.plot.facetgrid.FacetGrid at 0x2affc38316c0>

Test out available variables

1. KPP_Kheat is non-zero only in KPP_OBL

(mom6140.Tflx_dia_diff * 1025 * 4000).cf.plot(robust=True)

<matplotlib.collections.QuadMesh at 0x2ba58a988fa0>

mom6140.Kv_u.cf.plot(norm=mpl.colors.LogNorm(1e-6, 1e-2))

<matplotlib.collections.QuadMesh at 0x2ba58a007cd0>

mom6140.h.median("time").plot()
mom6140.h.mean("time").plot()

[<matplotlib.lines.Line2D at 0x2ba58a547c10>]

mom6140.Kd_heat.cf.plot(norm=mpl.colors.LogNorm(1e-6, 1e-2))
mom6140.KPP_OBLdepth.plot(color='r')

plt.figure()
mom6140.KPP_kheat.cf.plot(norm=mpl.colors.LogNorm(1e-6, 1e-2))
mom6140.KPP_OBLdepth.plot(color='r')

plt.figure()
(mom6140.KPP_kheat - mom6140.Kd_heat).cf.plot(robust=True, cmap=mpl.cm.mpl.cm.Reds_r, vmax=0)

<matplotlib.collections.QuadMesh at 0x2ba58a8c38b0>

Experiment with manual combining

import intake
from intake.source.utils import reverse_format

years = []
tiles = []
for file in files[:10]:
    p = pathlib.Path(file)
    params = reverse_format("__{year}.nc.{tile}", p.stem + p.suffix)
    years.append(params["year"])
    tiles.append(params["tile"])
years, tiles

import toolz as tlz

bytile = {}
for tile, v in tlz.groupby(tileid, files).items():
    bytile[tile] = xr.concat(read_raw_files(v, parallel=True), dim="time")



print("\n".join([hash(ds.yh.data.tolist()) for ds in list(bytile.values())]))

from functools import partial


def hash_coords(ds, axis):
    dims = ds.cf.axes[axis]
    data = np.concatenate(
        [ds[dim].data
        for dim in dims]
    )
    return hash(tuple(data))


grouped = bytile

for axis, concat_axis in [("X", "Y"), ("Y", "X")]:
    grouped = tlz.groupby(partial(hash_coords, axis=axis), grouped.values())
    grouped = {
        k: cfconcat(v, axis=concat_axis, join='exact') for k, v in grouped.items()
    }
    break 
grouped

cfconcat(list(grouped.values()), "X")

combined = xr.combine_by_coords(list(bytile.values()), combine_attrs="override")

def raise_if_bad_index(combined):
    bad = []
    for idx in combined.indexes:
        index = combined.indexes[idx]
        if not index.is_monotonic or index.has_duplicates:
            bad.append(idx)
    if bad:
        raise ValueError(f"Indexes {idx} are either not monotonic or have duplicates")

def tileid(path):
    p = pathlib.Path(path)
    #print(p.suffix)
    #params = reverse_format("__{year}.nc.{tile}", p.stem + p.suffix)
    return int(p.suffix[1:]) #params["tile"]
    #years.append(params["year"])
    #tiles.append(params["tile"])
    
sorted_files = sorted(files, key=tileid)
for tile, files in groupby(sorted_files, tileid):
    print(tile, len(list(files)))

Test out ONI calculation: close enough!

ersst = xr.tutorial.open_dataset("ersstv5")

monthlyersst = (
    ersst.sst.cf.sortby("Y")
    .cf.sel(latitude=slice(-5, 5), longitude=slice(360-170, 360-120))
    .cf.mean(["X", "Y"])
    .resample(time="M").mean()
    .load()
)

expected = mixpods.calc_oni(monthlyersst)

actual = pump.obs.process_oni()
actual.plot()
expected.plot()

(actual-expected).plot()

[<matplotlib.lines.Line2D at 0x2b6d2ec04580>]